Overview

Dataset statistics

Number of variables43
Number of observations1276
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory428.8 KiB
Average record size in memory344.1 B

Variable types

Numeric13
Categorical20
Boolean10

Alerts

Model has a high cardinality: 263 distinct values High cardinality
Variant has a high cardinality: 1064 distinct values High cardinality
Ex-Showroom_Price is highly correlated with Displacement and 6 other fieldsHigh correlation
Displacement is highly correlated with Ex-Showroom_Price and 6 other fieldsHigh correlation
Cylinders is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Fuel_Tank_Capacity is highly correlated with Ex-Showroom_Price and 6 other fieldsHigh correlation
Height is highly correlated with Ground_Clearance and 1 other fieldsHigh correlation
Length is highly correlated with Ex-Showroom_Price and 7 other fieldsHigh correlation
Width is highly correlated with Ex-Showroom_Price and 6 other fieldsHigh correlation
ARAI_Certified_Mileage is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Ground_Clearance is highly correlated with HeightHigh correlation
Power is highly correlated with Ex-Showroom_Price and 6 other fieldsHigh correlation
Seating_Capacity is highly correlated with HeightHigh correlation
Boot_Space is highly correlated with LengthHigh correlation
Ex-Showroom_Price is highly correlated with Displacement and 2 other fieldsHigh correlation
Displacement is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Cylinders is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Fuel_Tank_Capacity is highly correlated with Displacement and 4 other fieldsHigh correlation
Height is highly correlated with Seating_CapacityHigh correlation
Length is highly correlated with Displacement and 4 other fieldsHigh correlation
Width is highly correlated with Displacement and 4 other fieldsHigh correlation
Power is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Seating_Capacity is highly correlated with HeightHigh correlation
Ex-Showroom_Price is highly correlated with Displacement and 5 other fieldsHigh correlation
Displacement is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Cylinders is highly correlated with Ex-Showroom_Price and 4 other fieldsHigh correlation
Fuel_Tank_Capacity is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Height is highly correlated with Seating_CapacityHigh correlation
Length is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Width is highly correlated with Ex-Showroom_Price and 4 other fieldsHigh correlation
Power is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Seating_Capacity is highly correlated with HeightHigh correlation
Type is highly correlated with EBA_(Electronic_Brake_Assist) and 1 other fieldsHigh correlation
Door_Ajar_Warning is highly correlated with Central_Locking and 3 other fieldsHigh correlation
Gears is highly correlated with EBA_(Electronic_Brake_Assist) and 2 other fieldsHigh correlation
Central_Locking is highly correlated with Door_Ajar_Warning and 6 other fieldsHigh correlation
Keyless_Entry is highly correlated with Central_Locking and 1 other fieldsHigh correlation
EBA_(Electronic_Brake_Assist) is highly correlated with Type and 8 other fieldsHigh correlation
Body_Type is highly correlated with Fuel_SystemHigh correlation
Fuel_System is highly correlated with Gears and 2 other fieldsHigh correlation
Power_Windows is highly correlated with Door_Ajar_Warning and 2 other fieldsHigh correlation
EBD_(Electronic_Brake-force_Distribution) is highly correlated with ABS_(Anti-lock_Braking_System)High correlation
Power_Steering is highly correlated with MakeHigh correlation
Make is highly correlated with Door_Ajar_Warning and 7 other fieldsHigh correlation
Audiosystem is highly correlated with Central_Locking and 1 other fieldsHigh correlation
Power_Seats is highly correlated with EBA_(Electronic_Brake_Assist) and 2 other fieldsHigh correlation
Seat_Height_Adjustment is highly correlated with EBA_(Electronic_Brake_Assist) and 1 other fieldsHigh correlation
ABS_(Anti-lock_Braking_System) is highly correlated with Central_Locking and 1 other fieldsHigh correlation
Parking_Assistance is highly correlated with EBA_(Electronic_Brake_Assist) and 2 other fieldsHigh correlation
Handbrake is highly correlated with Type and 8 other fieldsHigh correlation
Bluetooth is highly correlated with Central_Locking and 5 other fieldsHigh correlation
Ventilation_System is highly correlated with Door_Ajar_Warning and 5 other fieldsHigh correlation
Emission_Norm is highly correlated with MakeHigh correlation
Engine_Immobilizer is highly correlated with Make and 1 other fieldsHigh correlation
Seats_Material is highly correlated with EBA_(Electronic_Brake_Assist) and 1 other fieldsHigh correlation
Unnamed: 0 is highly correlated with Make and 14 other fieldsHigh correlation
Make is highly correlated with Unnamed: 0 and 35 other fieldsHigh correlation
Ex-Showroom_Price is highly correlated with Make and 10 other fieldsHigh correlation
Displacement is highly correlated with Unnamed: 0 and 19 other fieldsHigh correlation
Cylinders is highly correlated with Unnamed: 0 and 15 other fieldsHigh correlation
Emission_Norm is highly correlated with Make and 2 other fieldsHigh correlation
Fuel_System is highly correlated with Make and 5 other fieldsHigh correlation
Fuel_Tank_Capacity is highly correlated with Unnamed: 0 and 16 other fieldsHigh correlation
Fuel_Type is highly correlated with MakeHigh correlation
Height is highly correlated with Make and 9 other fieldsHigh correlation
Length is highly correlated with Unnamed: 0 and 16 other fieldsHigh correlation
Width is highly correlated with Unnamed: 0 and 17 other fieldsHigh correlation
Body_Type is highly correlated with Unnamed: 0 and 15 other fieldsHigh correlation
Gears is highly correlated with Make and 14 other fieldsHigh correlation
Ground_Clearance is highly correlated with Make and 6 other fieldsHigh correlation
Power_Steering is highly correlated with Make and 9 other fieldsHigh correlation
Power_Windows is highly correlated with Make and 6 other fieldsHigh correlation
Power_Seats is highly correlated with Unnamed: 0 and 15 other fieldsHigh correlation
Keyless_Entry is highly correlated with Make and 14 other fieldsHigh correlation
Power is highly correlated with Unnamed: 0 and 19 other fieldsHigh correlation
Seating_Capacity is highly correlated with Make and 7 other fieldsHigh correlation
Seats_Material is highly correlated with Unnamed: 0 and 13 other fieldsHigh correlation
Type is highly correlated with Unnamed: 0 and 5 other fieldsHigh correlation
Audiosystem is highly correlated with Make and 15 other fieldsHigh correlation
Bluetooth is highly correlated with Make and 11 other fieldsHigh correlation
Boot_Space is highly correlated with Make and 3 other fieldsHigh correlation
Central_Locking is highly correlated with Make and 9 other fieldsHigh correlation
Child_Safety_Locks is highly correlated with MakeHigh correlation
Handbrake is highly correlated with Unnamed: 0 and 18 other fieldsHigh correlation
Instrument_Console is highly correlated with MakeHigh correlation
Ventilation_System is highly correlated with Unnamed: 0 and 25 other fieldsHigh correlation
Engine_Immobilizer is highly correlated with Make and 3 other fieldsHigh correlation
ABS_(Anti-lock_Braking_System) is highly correlated with Keyless_Entry and 7 other fieldsHigh correlation
Door_Ajar_Warning is highly correlated with Make and 9 other fieldsHigh correlation
EBD_(Electronic_Brake-force_Distribution) is highly correlated with Make and 6 other fieldsHigh correlation
Fasten_Seat_Belt_Warning is highly correlated with Width and 8 other fieldsHigh correlation
Parking_Assistance is highly correlated with Make and 10 other fieldsHigh correlation
EBA_(Electronic_Brake_Assist) is highly correlated with Unnamed: 0 and 15 other fieldsHigh correlation
Seat_Height_Adjustment is highly correlated with Unnamed: 0 and 17 other fieldsHigh correlation
ARAI_Certified_Mileage is highly skewed (γ1 = 33.64370862) Skewed
Unnamed: 0 is uniformly distributed Uniform
Variant is uniformly distributed Uniform
Unnamed: 0 has unique values Unique

Reproduction

Analysis started2022-05-21 13:28:09.597572
Analysis finished2022-05-21 13:29:19.205049
Duration1 minute and 9.61 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean637.5
Minimum0
Maximum1275
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-21T18:59:19.419451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile63.75
Q1318.75
median637.5
Q3956.25
95-th percentile1211.25
Maximum1275
Range1275
Interquartile range (IQR)637.5

Descriptive statistics

Standard deviation368.493781
Coefficient of variation (CV)0.5780294605
Kurtosis-1.2
Mean637.5
Median Absolute Deviation (MAD)319
Skewness0
Sum813450
Variance135787.6667
MonotonicityStrictly increasing
2022-05-21T18:59:19.724394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
8481
 
0.1%
8551
 
0.1%
8541
 
0.1%
8531
 
0.1%
8521
 
0.1%
8511
 
0.1%
8501
 
0.1%
8491
 
0.1%
8471
 
0.1%
Other values (1266)1266
99.2%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
12751
0.1%
12741
0.1%
12731
0.1%
12721
0.1%
12711
0.1%
12701
0.1%
12691
0.1%
12681
0.1%
12671
0.1%
12661
0.1%

Make
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct39
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Maruti Suzuki
224 
Hyundai
130 
Mahindra
119 
Tata
100 
Toyota
82 
Other values (34)
621 

Length

Max length16
Median length13
Mean length7.441222571
Min length2

Characters and Unicode

Total characters9495
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTata
2nd rowTata
3rd rowTata
4th rowTata
5th rowTata

Common Values

ValueCountFrequency (%)
Maruti Suzuki224
17.6%
Hyundai130
 
10.2%
Mahindra119
 
9.3%
Tata100
 
7.8%
Toyota82
 
6.4%
Honda64
 
5.0%
Skoda43
 
3.4%
Ford43
 
3.4%
Bmw37
 
2.9%
Renault36
 
2.8%
Other values (29)398
31.2%

Length

2022-05-21T18:59:20.046041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maruti238
14.9%
suzuki238
14.9%
hyundai130
 
8.2%
mahindra119
 
7.5%
tata100
 
6.3%
toyota82
 
5.1%
honda64
 
4.0%
rover64
 
4.0%
skoda43
 
2.7%
ford43
 
2.7%
Other values (31)474
29.7%

Most occurring characters

ValueCountFrequency (%)
a1282
13.5%
u977
 
10.3%
i924
 
9.7%
r567
 
6.0%
t526
 
5.5%
n499
 
5.3%
o484
 
5.1%
d467
 
4.9%
M399
 
4.2%
319
 
3.4%
Other values (31)3051
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7581
79.8%
Uppercase Letter1595
 
16.8%
Space Separator319
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1282
16.9%
u977
12.9%
i924
12.2%
r567
7.5%
t526
6.9%
n499
 
6.6%
o484
 
6.4%
d467
 
6.2%
k315
 
4.2%
e261
 
3.4%
Other values (14)1279
16.9%
Uppercase Letter
ValueCountFrequency (%)
M399
25.0%
S281
17.6%
H194
12.2%
T182
11.4%
R114
 
7.1%
F80
 
5.0%
L60
 
3.8%
V52
 
3.3%
J50
 
3.1%
B47
 
2.9%
Other values (6)136
 
8.5%
Space Separator
ValueCountFrequency (%)
319
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9176
96.6%
Common319
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1282
14.0%
u977
 
10.6%
i924
 
10.1%
r567
 
6.2%
t526
 
5.7%
n499
 
5.4%
o484
 
5.3%
d467
 
5.1%
M399
 
4.3%
k315
 
3.4%
Other values (30)2736
29.8%
Common
ValueCountFrequency (%)
319
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1282
13.5%
u977
 
10.3%
i924
 
9.7%
r567
 
6.0%
t526
 
5.5%
n499
 
5.3%
o484
 
5.1%
d467
 
4.9%
M399
 
4.2%
319
 
3.4%
Other values (31)3051
32.1%

Model
Categorical

HIGH CARDINALITY

Distinct263
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Nexon
 
24
Kuv100 Nxt
 
22
Xuv500
 
21
Compass
 
21
Amaze
 
20
Other values (258)
1168 

Length

Max length33
Median length26
Mean length7.568965517
Min length2

Characters and Unicode

Total characters9658
Distinct characters67
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)5.3%

Sample

1st rowNano Genx
2nd rowNano Genx
3rd rowNano Genx
4th rowNano Genx
5th rowNano Genx

Common Values

ValueCountFrequency (%)
Nexon24
 
1.9%
Kuv100 Nxt22
 
1.7%
Xuv50021
 
1.6%
Compass21
 
1.6%
Amaze20
 
1.6%
Creta18
 
1.4%
Innova Crysta16
 
1.3%
Seltos16
 
1.3%
Etios Liva14
 
1.1%
Swift14
 
1.1%
Other values (253)1090
85.4%

Length

2022-05-21T18:59:20.349284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mercedes-benz57
 
3.4%
etios30
 
1.8%
range27
 
1.6%
nexon27
 
1.6%
grand26
 
1.5%
compass23
 
1.4%
kuv10022
 
1.3%
nxt22
 
1.3%
i1022
 
1.3%
xuv50021
 
1.2%
Other values (279)1404
83.5%

Most occurring characters

ValueCountFrequency (%)
e897
 
9.3%
r695
 
7.2%
o671
 
6.9%
a656
 
6.8%
s519
 
5.4%
i460
 
4.8%
t459
 
4.8%
n450
 
4.7%
405
 
4.2%
l302
 
3.1%
Other values (57)4144
42.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6778
70.2%
Uppercase Letter1824
 
18.9%
Decimal Number464
 
4.8%
Space Separator405
 
4.2%
Dash Punctuation174
 
1.8%
Math Symbol9
 
0.1%
Open Punctuation2
 
< 0.1%
Close Punctuation2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e897
13.2%
r695
10.3%
o671
9.9%
a656
9.7%
s519
 
7.7%
i460
 
6.8%
t459
 
6.8%
n450
 
6.6%
l302
 
4.5%
u241
 
3.6%
Other values (16)1428
21.1%
Uppercase Letter
ValueCountFrequency (%)
C247
13.5%
S137
 
7.5%
A124
 
6.8%
E123
 
6.7%
B113
 
6.2%
G105
 
5.8%
X103
 
5.6%
M88
 
4.8%
P86
 
4.7%
V85
 
4.7%
Other values (16)613
33.6%
Decimal Number
ValueCountFrequency (%)
0211
45.5%
164
 
13.8%
546
 
9.9%
340
 
8.6%
426
 
5.6%
220
 
4.3%
718
 
3.9%
616
 
3.4%
814
 
3.0%
99
 
1.9%
Space Separator
ValueCountFrequency (%)
405
100.0%
Dash Punctuation
ValueCountFrequency (%)
-174
100.0%
Math Symbol
ValueCountFrequency (%)
+9
100.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8602
89.1%
Common1056
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e897
 
10.4%
r695
 
8.1%
o671
 
7.8%
a656
 
7.6%
s519
 
6.0%
i460
 
5.3%
t459
 
5.3%
n450
 
5.2%
l302
 
3.5%
C247
 
2.9%
Other values (42)3246
37.7%
Common
ValueCountFrequency (%)
405
38.4%
0211
20.0%
-174
16.5%
164
 
6.1%
546
 
4.4%
340
 
3.8%
426
 
2.5%
220
 
1.9%
718
 
1.7%
616
 
1.5%
Other values (5)36
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII9658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e897
 
9.3%
r695
 
7.2%
o671
 
6.9%
a656
 
6.8%
s519
 
5.4%
i460
 
4.8%
t459
 
4.8%
n450
 
4.7%
405
 
4.2%
l302
 
3.1%
Other values (57)4144
42.9%

Variant
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1064
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Lxi
 
9
Vxi
 
9
Coupe
 
8
S
 
7
Xe Diesel
 
6
Other values (1059)
1237 

Length

Max length38
Median length27
Mean length11.98275862
Min length1

Characters and Unicode

Total characters15290
Distinct characters68
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique934 ?
Unique (%)73.2%

Sample

1st rowXt
2nd rowXe
3rd rowEmax Xm
4th rowXta
5th rowXm

Common Values

ValueCountFrequency (%)
Lxi9
 
0.7%
Vxi9
 
0.7%
Coupe8
 
0.6%
S7
 
0.5%
Xe Diesel6
 
0.5%
V86
 
0.5%
V5
 
0.4%
Zxi5
 
0.4%
S Diesel4
 
0.3%
Vx4
 
0.3%
Other values (1054)1213
95.1%

Length

2022-05-21T18:59:20.646428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
diesel112
 
3.3%
plus105
 
3.1%
petrol94
 
2.7%
at92
 
2.7%
o91
 
2.7%
1.275
 
2.2%
mt67
 
2.0%
s55
 
1.6%
1.544
 
1.3%
amt43
 
1.3%
Other values (438)2648
77.3%

Most occurring characters

ValueCountFrequency (%)
2150
 
14.1%
t981
 
6.4%
i895
 
5.9%
e873
 
5.7%
r592
 
3.9%
l543
 
3.6%
o492
 
3.2%
.422
 
2.8%
s418
 
2.7%
n368
 
2.4%
Other values (58)7556
49.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7784
50.9%
Uppercase Letter3035
 
19.8%
Space Separator2150
 
14.1%
Decimal Number1556
 
10.2%
Other Punctuation426
 
2.8%
Open Punctuation129
 
0.8%
Close Punctuation123
 
0.8%
Dash Punctuation51
 
0.3%
Math Symbol36
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t981
12.6%
i895
11.5%
e873
11.2%
r592
 
7.6%
l543
 
7.0%
o492
 
6.3%
s418
 
5.4%
n368
 
4.7%
d365
 
4.7%
a350
 
4.5%
Other values (16)1907
24.5%
Uppercase Letter
ValueCountFrequency (%)
S350
11.5%
D303
10.0%
P285
9.4%
T259
 
8.5%
A231
 
7.6%
L212
 
7.0%
V195
 
6.4%
C194
 
6.4%
X177
 
5.8%
M134
 
4.4%
Other values (15)695
22.9%
Decimal Number
ValueCountFrequency (%)
1358
23.0%
2283
18.2%
0245
15.7%
4167
10.7%
5165
10.6%
693
 
6.0%
393
 
6.0%
889
 
5.7%
749
 
3.1%
914
 
0.9%
Other Punctuation
ValueCountFrequency (%)
.422
99.1%
&4
 
0.9%
Space Separator
ValueCountFrequency (%)
2150
100.0%
Open Punctuation
ValueCountFrequency (%)
(129
100.0%
Close Punctuation
ValueCountFrequency (%)
)123
100.0%
Dash Punctuation
ValueCountFrequency (%)
-51
100.0%
Math Symbol
ValueCountFrequency (%)
+36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10819
70.8%
Common4471
29.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t981
 
9.1%
i895
 
8.3%
e873
 
8.1%
r592
 
5.5%
l543
 
5.0%
o492
 
4.5%
s418
 
3.9%
n368
 
3.4%
d365
 
3.4%
S350
 
3.2%
Other values (41)4942
45.7%
Common
ValueCountFrequency (%)
2150
48.1%
.422
 
9.4%
1358
 
8.0%
2283
 
6.3%
0245
 
5.5%
4167
 
3.7%
5165
 
3.7%
(129
 
2.9%
)123
 
2.8%
693
 
2.1%
Other values (7)336
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII15290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2150
 
14.1%
t981
 
6.4%
i895
 
5.9%
e873
 
5.7%
r592
 
3.9%
l543
 
3.6%
o492
 
3.2%
.422
 
2.8%
s418
 
2.7%
n368
 
2.4%
Other values (58)7556
49.4%

Ex-Showroom_Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1179
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4596537.887
Minimum236447
Maximum212155397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-21T18:59:20.967263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum236447
5-th percentile467686
Q1743876
median1060064.5
Q32979827.75
95-th percentile21624349.5
Maximum212155397
Range211918950
Interquartile range (IQR)2235951.75

Descriptive statistics

Standard deviation12147346.94
Coefficient of variation (CV)2.642716593
Kurtosis118.5505817
Mean4596537.887
Median Absolute Deviation (MAD)469101
Skewness8.785168691
Sum5865182344
Variance1.475580378 × 1014
MonotonicityNot monotonic
2022-05-21T18:59:21.271018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99990014
 
1.1%
9999904
 
0.3%
9250003
 
0.2%
9890003
 
0.2%
7450003
 
0.2%
6500002
 
0.2%
32995992
 
0.2%
9500002
 
0.2%
5368592
 
0.2%
23595992
 
0.2%
Other values (1169)1239
97.1%
ValueCountFrequency (%)
2364471
0.1%
2630001
0.1%
2722231
0.1%
2796501
0.1%
2827781
0.1%
2830001
0.1%
2832901
0.1%
2844851
0.1%
2926671
0.1%
2948001
0.1%
ValueCountFrequency (%)
2121553971
0.1%
1921429371
0.1%
950000001
0.1%
837553831
0.1%
773126611
0.1%
754000001
0.1%
695000001
0.1%
592161931
0.1%
532472011
0.1%
532103271
0.1%

Displacement
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct130
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1858.640191
Minimum72
Maximum7993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-21T18:59:21.560647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile998
Q11198
median1497
Q31998
95-th percentile3996
Maximum7993
Range7921
Interquartile range (IQR)800

Descriptive statistics

Standard deviation1058.138838
Coefficient of variation (CV)0.569308058
Kurtosis7.797705278
Mean1858.640191
Median Absolute Deviation (MAD)301
Skewness2.562423249
Sum2371624.884
Variance1119657.8
MonotonicityNot monotonic
2022-05-21T18:59:21.872598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1197108
 
8.5%
149887
 
6.8%
124862
 
4.9%
119858
 
4.5%
149756
 
4.4%
99853
 
4.2%
217946
 
3.6%
119939
 
3.1%
146138
 
3.0%
196829
 
2.3%
Other values (120)700
54.9%
ValueCountFrequency (%)
723
 
0.2%
2162
 
0.2%
6246
 
0.5%
79612
 
0.9%
7998
 
0.6%
99853
4.2%
99920
 
1.6%
10471
 
0.1%
10868
 
0.6%
11205
 
0.4%
ValueCountFrequency (%)
79932
0.2%
67521
 
0.1%
67501
 
0.1%
67494
0.3%
65981
 
0.1%
65931
 
0.1%
65923
0.2%
64983
0.2%
64961
 
0.1%
64171
 
0.1%

Cylinders
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.361285266
Minimum2
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-21T18:59:22.229542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median4
Q34
95-th percentile8
Maximum16
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.619597503
Coefficient of variation (CV)0.3713578462
Kurtosis11.80080164
Mean4.361285266
Median Absolute Deviation (MAD)0
Skewness3.082730773
Sum5565
Variance2.623096072
MonotonicityNot monotonic
2022-05-21T18:59:22.427981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4855
67.0%
3213
 
16.7%
6106
 
8.3%
853
 
4.2%
1221
 
1.6%
510
 
0.8%
1010
 
0.8%
26
 
0.5%
162
 
0.2%
ValueCountFrequency (%)
26
 
0.5%
3213
 
16.7%
4855
67.0%
510
 
0.8%
6106
 
8.3%
853
 
4.2%
1010
 
0.8%
1221
 
1.6%
162
 
0.2%
ValueCountFrequency (%)
162
 
0.2%
1221
 
1.6%
1010
 
0.8%
853
 
4.2%
6106
 
8.3%
510
 
0.8%
4855
67.0%
3213
 
16.7%
26
 
0.5%

Emission_Norm
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
BS IV
890 
BS VI
377 
BS III
 
9

Length

Max length6
Median length5
Mean length5.007053292
Min length5

Characters and Unicode

Total characters6389
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBS IV
2nd rowBS IV
3rd rowBS IV
4th rowBS IV
5th rowBS IV

Common Values

ValueCountFrequency (%)
BS IV890
69.7%
BS VI377
29.5%
BS III9
 
0.7%

Length

2022-05-21T18:59:22.734799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-21T18:59:23.021531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
bs1276
50.0%
iv890
34.9%
vi377
 
14.8%
iii9
 
0.4%

Most occurring characters

ValueCountFrequency (%)
I1294
20.3%
B1276
20.0%
S1276
20.0%
1276
20.0%
V1267
19.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5113
80.0%
Space Separator1276
 
20.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I1294
25.3%
B1276
25.0%
S1276
25.0%
V1267
24.8%
Space Separator
ValueCountFrequency (%)
1276
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5113
80.0%
Common1276
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I1294
25.3%
B1276
25.0%
S1276
25.0%
V1267
24.8%
Common
ValueCountFrequency (%)
1276
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6389
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I1294
20.3%
B1276
20.0%
S1276
20.0%
1276
20.0%
V1267
19.8%

Fuel_System
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Injection
1272 
PGM - Fi
 
4

Length

Max length9
Median length9
Mean length8.996865204
Min length8

Characters and Unicode

Total characters11480
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInjection
2nd rowInjection
3rd rowInjection
4th rowInjection
5th rowInjection

Common Values

ValueCountFrequency (%)
Injection1272
99.7%
PGM - Fi4
 
0.3%

Length

2022-05-21T18:59:23.262678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-21T18:59:23.522712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
injection1272
99.1%
pgm4
 
0.3%
4
 
0.3%
fi4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n2544
22.2%
i1276
11.1%
I1272
11.1%
j1272
11.1%
e1272
11.1%
c1272
11.1%
t1272
11.1%
o1272
11.1%
8
 
0.1%
P4
 
< 0.1%
Other values (4)16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10180
88.7%
Uppercase Letter1288
 
11.2%
Space Separator8
 
0.1%
Dash Punctuation4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n2544
25.0%
i1276
12.5%
j1272
12.5%
e1272
12.5%
c1272
12.5%
t1272
12.5%
o1272
12.5%
Uppercase Letter
ValueCountFrequency (%)
I1272
98.8%
P4
 
0.3%
G4
 
0.3%
M4
 
0.3%
F4
 
0.3%
Space Separator
ValueCountFrequency (%)
8
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11468
99.9%
Common12
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n2544
22.2%
i1276
11.1%
I1272
11.1%
j1272
11.1%
e1272
11.1%
c1272
11.1%
t1272
11.1%
o1272
11.1%
P4
 
< 0.1%
G4
 
< 0.1%
Other values (2)8
 
0.1%
Common
ValueCountFrequency (%)
8
66.7%
-4
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n2544
22.2%
i1276
11.1%
I1272
11.1%
j1272
11.1%
e1272
11.1%
c1272
11.1%
t1272
11.1%
o1272
11.1%
8
 
0.1%
P4
 
< 0.1%
Other values (4)16
 
0.1%

Fuel_Tank_Capacity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct64
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.96681778
Minimum15
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-21T18:59:23.772943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile35
Q141
median49.30086207
Q360
95-th percentile85
Maximum105
Range90
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.02009752
Coefficient of variation (CV)0.3082755152
Kurtosis0.5327860481
Mean51.96681778
Median Absolute Deviation (MAD)9.30086207
Skewness0.9895273625
Sum66309.65948
Variance256.6435246
MonotonicityNot monotonic
2022-05-21T18:59:24.095165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45125
 
9.8%
35124
 
9.7%
5094
 
7.4%
6091
 
7.1%
3779
 
6.2%
5573
 
5.7%
49.3008620769
 
5.4%
4056
 
4.4%
4251
 
4.0%
7048
 
3.8%
Other values (54)466
36.5%
ValueCountFrequency (%)
151
 
0.1%
245
 
0.4%
2710
 
0.8%
2818
 
1.4%
3221
 
1.6%
35124
9.7%
361
 
0.1%
3779
6.2%
4056
4.4%
4114
 
1.1%
ValueCountFrequency (%)
1056
 
0.5%
1007
 
0.5%
961
 
0.1%
93.54
 
0.3%
937
 
0.5%
922
 
0.2%
912
 
0.2%
90.51
 
0.1%
9021
1.6%
894
 
0.3%

Fuel_Type
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Petrol
643 
Diesel
582 
CNG
 
16
Hybrid
 
15
Electric
 
14

Length

Max length12
Median length6
Mean length6.012539185
Min length3

Characters and Unicode

Total characters7672
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPetrol
2nd rowPetrol
3rd rowCNG
4th rowPetrol
5th rowPetrol

Common Values

ValueCountFrequency (%)
Petrol643
50.4%
Diesel582
45.6%
CNG16
 
1.3%
Hybrid15
 
1.2%
Electric14
 
1.1%
CNG + Petrol6
 
0.5%

Length

2022-05-21T18:59:24.394395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-21T18:59:24.712537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
petrol649
50.4%
diesel582
45.2%
cng22
 
1.7%
hybrid15
 
1.2%
electric14
 
1.1%
6
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e1827
23.8%
l1245
16.2%
r678
 
8.8%
t663
 
8.6%
P649
 
8.5%
o649
 
8.5%
i611
 
8.0%
D582
 
7.6%
s582
 
7.6%
c28
 
0.4%
Other values (10)158
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6328
82.5%
Uppercase Letter1326
 
17.3%
Space Separator12
 
0.2%
Math Symbol6
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1827
28.9%
l1245
19.7%
r678
 
10.7%
t663
 
10.5%
o649
 
10.3%
i611
 
9.7%
s582
 
9.2%
c28
 
0.4%
y15
 
0.2%
b15
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
P649
48.9%
D582
43.9%
G22
 
1.7%
N22
 
1.7%
C22
 
1.7%
H15
 
1.1%
E14
 
1.1%
Space Separator
ValueCountFrequency (%)
12
100.0%
Math Symbol
ValueCountFrequency (%)
+6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7654
99.8%
Common18
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1827
23.9%
l1245
16.3%
r678
 
8.9%
t663
 
8.7%
P649
 
8.5%
o649
 
8.5%
i611
 
8.0%
D582
 
7.6%
s582
 
7.6%
c28
 
0.4%
Other values (8)140
 
1.8%
Common
ValueCountFrequency (%)
12
66.7%
+6
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1827
23.8%
l1245
16.2%
r678
 
8.8%
t663
 
8.6%
P649
 
8.5%
o649
 
8.5%
i611
 
8.0%
D582
 
7.6%
s582
 
7.6%
c28
 
0.4%
Other values (10)158
 
2.1%

Height
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct211
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1591.03828
Minimum1.845
Maximum2670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-21T18:59:24.977477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.845
5-th percentile1414.75
Q11495
median1557
Q31666
95-th percentile1870
Maximum2670
Range2668.155
Interquartile range (IQR)171

Descriptive statistics

Standard deviation157.8668681
Coefficient of variation (CV)0.09922254548
Kurtosis9.60200882
Mean1591.03828
Median Absolute Deviation (MAD)83
Skewness-0.2407282096
Sum2030164.845
Variance24921.94803
MonotonicityNot monotonic
2022-05-21T18:59:25.311938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152050
 
3.9%
151035
 
2.7%
164033
 
2.6%
156029
 
2.3%
160727
 
2.1%
152527
 
2.1%
149524
 
1.9%
165522
 
1.7%
147522
 
1.7%
151521
 
1.6%
Other values (201)986
77.3%
ValueCountFrequency (%)
1.8451
 
0.1%
11363
 
0.2%
11659
0.7%
12001
 
0.1%
12031
 
0.1%
12111
 
0.1%
12122
 
0.2%
12132
 
0.2%
12501
 
0.1%
12521
 
0.1%
ValueCountFrequency (%)
26701
 
0.1%
20752
 
0.2%
20554
0.3%
19956
0.5%
19772
 
0.2%
19691
 
0.1%
19381
 
0.1%
19304
0.3%
19224
0.3%
19103
0.2%

Length
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct228
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4296.88373
Minimum4.64
Maximum6092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-21T18:59:25.643286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4.64
5-th percentile3607.5
Q13991.75
median4331
Q34620
95-th percentile5115
Maximum6092
Range6087.36
Interquartile range (IQR)628.25

Descriptive statistics

Standard deviation476.6129775
Coefficient of variation (CV)0.110920613
Kurtosis4.644522361
Mean4296.88373
Median Absolute Deviation (MAD)336
Skewness-0.3364237545
Sum5482823.64
Variance227159.9303
MonotonicityNot monotonic
2022-05-21T18:59:25.982860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3995174
 
13.6%
444036
 
2.8%
458529
 
2.3%
370029
 
2.3%
399427
 
2.1%
439527
 
2.1%
431525
 
2.0%
360020
 
1.6%
441318
 
1.4%
427018
 
1.4%
Other values (218)873
68.4%
ValueCountFrequency (%)
4.641
 
0.1%
27522
 
0.2%
31646
0.5%
33702
 
0.2%
33902
 
0.2%
33951
 
0.1%
34296
0.5%
34301
 
0.1%
34458
0.6%
35455
0.4%
ValueCountFrequency (%)
60921
0.1%
58422
0.2%
56121
0.1%
55751
0.1%
55691
0.1%
54581
0.1%
54531
0.1%
53991
0.1%
53701
0.1%
53411
0.1%

Width
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct183
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1788.392326
Minimum1.845
Maximum2226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-21T18:59:26.297833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.845
5-th percentile1579.75
Q11698
median1771.730286
Q31850.5
95-th percentile2091
Maximum2226
Range2224.155
Interquartile range (IQR)152.5

Descriptive statistics

Standard deviation150.229215
Coefficient of variation (CV)0.0840023818
Kurtosis16.29276096
Mean1788.392326
Median Absolute Deviation (MAD)76.730286
Skewness-0.6533569595
Sum2281988.608
Variance22568.81704
MonotonicityNot monotonic
2022-05-21T18:59:26.589651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169576
 
6.0%
173570
 
5.5%
173036
 
2.8%
181130
 
2.4%
174529
 
2.3%
169928
 
2.2%
166028
 
2.2%
185025
 
2.0%
189024
 
1.9%
181823
 
1.8%
Other values (173)907
71.1%
ValueCountFrequency (%)
1.8451
 
0.1%
13122
 
0.2%
14102
 
0.2%
14591
 
0.1%
14756
 
0.5%
149015
1.2%
152010
0.8%
15402
 
0.2%
15606
 
0.5%
15706
 
0.5%
ValueCountFrequency (%)
22261
 
0.1%
222014
1.1%
22183
 
0.2%
22081
 
0.1%
22072
 
0.2%
22008
0.6%
21942
 
0.2%
21811
 
0.1%
21752
 
0.2%
21694
 
0.3%

Body_Type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
SUV
453 
Sedan
333 
Hatchback
316 
Coupe
 
41
MPV
 
39
Other values (12)
94 

Length

Max length19
Median length18
Mean length5.405172414
Min length3

Characters and Unicode

Total characters6897
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowHatchback
2nd rowHatchback
3rd rowHatchback
4th rowHatchback
5th rowHatchback

Common Values

ValueCountFrequency (%)
SUV453
35.5%
Sedan333
26.1%
Hatchback316
24.8%
Coupe41
 
3.2%
MPV39
 
3.1%
MUV39
 
3.1%
Convertible20
 
1.6%
Crossover18
 
1.4%
Pick-up3
 
0.2%
Sports3
 
0.2%
Other values (7)11
 
0.9%

Length

2022-05-21T18:59:26.912808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
suv457
35.5%
sedan336
26.1%
hatchback317
24.6%
coupe44
 
3.4%
mpv39
 
3.0%
muv39
 
3.0%
convertible23
 
1.8%
crossover23
 
1.8%
sports6
 
0.5%
pick-up3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a970
14.1%
S799
11.6%
c637
 
9.2%
V535
 
7.8%
U496
 
7.2%
e449
 
6.5%
n359
 
5.2%
t346
 
5.0%
b340
 
4.9%
d336
 
4.9%
Other values (17)1630
23.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4515
65.5%
Uppercase Letter2357
34.2%
Other Punctuation11
 
0.2%
Space Separator11
 
0.2%
Dash Punctuation3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a970
21.5%
c637
14.1%
e449
9.9%
n359
 
8.0%
t346
 
7.7%
b340
 
7.5%
d336
 
7.4%
k320
 
7.1%
h317
 
7.0%
o119
 
2.6%
Other values (7)322
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
S799
33.9%
V535
22.7%
U496
21.0%
H317
 
13.4%
C90
 
3.8%
M78
 
3.3%
P42
 
1.8%
Other Punctuation
ValueCountFrequency (%)
,11
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6872
99.6%
Common25
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a970
14.1%
S799
11.6%
c637
 
9.3%
V535
 
7.8%
U496
 
7.2%
e449
 
6.5%
n359
 
5.2%
t346
 
5.0%
b340
 
4.9%
d336
 
4.9%
Other values (14)1605
23.4%
Common
ValueCountFrequency (%)
,11
44.0%
11
44.0%
-3
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6897
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a970
14.1%
S799
11.6%
c637
 
9.2%
V535
 
7.8%
U496
 
7.2%
e449
 
6.5%
n359
 
5.2%
t346
 
5.0%
b340
 
4.9%
d336
 
4.9%
Other values (17)1630
23.6%

ARAI_Certified_Mileage
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct281
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.90211704
Minimum3.4
Maximum1449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-21T18:59:27.201841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.4
5-th percentile10.0075
Q116
median19.01
Q321.04
95-th percentile25.32
Maximum1449
Range1445.6
Interquartile range (IQR)5.04

Descriptive statistics

Standard deviation40.87928398
Coefficient of variation (CV)2.054016861
Kurtosis1174.22243
Mean19.90211704
Median Absolute Deviation (MAD)2.67
Skewness33.64370862
Sum25395.10134
Variance1671.115859
MonotonicityNot monotonic
2022-05-21T18:59:27.479018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.90211704114
 
8.9%
2327
 
2.1%
1624
 
1.9%
17.124
 
1.9%
17.623
 
1.8%
23.120
 
1.6%
2019
 
1.5%
15.418
 
1.4%
20.417
 
1.3%
23.5915
 
1.2%
Other values (271)975
76.4%
ValueCountFrequency (%)
3.41
 
0.1%
42
 
0.2%
52
 
0.2%
5.55
0.4%
5.952
 
0.2%
6.711
 
0.1%
7.291
 
0.1%
7.42
 
0.2%
7.62
 
0.2%
7.88
0.6%
ValueCountFrequency (%)
14491
 
0.1%
1424
0.3%
351
 
0.1%
28.48
0.6%
28.094
0.3%
27.48
0.6%
27.394
0.3%
27.33
 
0.2%
27.281
 
0.1%
26.821
 
0.1%

Gears
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
5
719 
6
233 
8
139 
7
137 
9
 
30
Other values (3)
 
18

Length

Max length27
Median length1
Mean length1.029780564
Min length1

Characters and Unicode

Total characters1314
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row4
2nd row4
3rd row4
4th row5
5th row4

Common Values

ValueCountFrequency (%)
5719
56.3%
6233
 
18.3%
8139
 
10.9%
7137
 
10.7%
930
 
2.4%
416
 
1.3%
Single Speed Reduction Gear1
 
0.1%
7 Dual Clutch1
 
0.1%

Length

2022-05-21T18:59:27.833596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-21T18:59:28.170920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
5719
56.1%
6233
 
18.2%
8139
 
10.9%
7138
 
10.8%
930
 
2.3%
416
 
1.2%
single1
 
0.1%
speed1
 
0.1%
reduction1
 
0.1%
gear1
 
0.1%
Other values (2)2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
5719
54.7%
6233
 
17.7%
8139
 
10.6%
7138
 
10.5%
930
 
2.3%
416
 
1.2%
e5
 
0.4%
5
 
0.4%
u3
 
0.2%
l3
 
0.2%
Other values (16)23
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1275
97.0%
Lowercase Letter28
 
2.1%
Uppercase Letter6
 
0.5%
Space Separator5
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5
17.9%
u3
10.7%
l3
10.7%
a2
 
7.1%
n2
 
7.1%
i2
 
7.1%
d2
 
7.1%
c2
 
7.1%
t2
 
7.1%
r1
 
3.6%
Other values (4)4
14.3%
Decimal Number
ValueCountFrequency (%)
5719
56.4%
6233
 
18.3%
8139
 
10.9%
7138
 
10.8%
930
 
2.4%
416
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
S2
33.3%
C1
16.7%
D1
16.7%
G1
16.7%
R1
16.7%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1280
97.4%
Latin34
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5
14.7%
u3
 
8.8%
l3
 
8.8%
a2
 
5.9%
n2
 
5.9%
i2
 
5.9%
d2
 
5.9%
S2
 
5.9%
c2
 
5.9%
t2
 
5.9%
Other values (9)9
26.5%
Common
ValueCountFrequency (%)
5719
56.2%
6233
 
18.2%
8139
 
10.9%
7138
 
10.8%
930
 
2.3%
416
 
1.2%
5
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5719
54.7%
6233
 
17.7%
8139
 
10.6%
7138
 
10.5%
930
 
2.3%
416
 
1.2%
e5
 
0.4%
5
 
0.4%
u3
 
0.2%
l3
 
0.2%
Other values (16)23
 
1.8%

Ground_Clearance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct73
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.3001567
Minimum100
Maximum498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-21T18:59:28.600325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile151.5
Q1165
median165
Q3188
95-th percentile210
Maximum498
Range398
Interquartile range (IQR)23

Descriptive statistics

Standard deviation29.75226523
Coefficient of variation (CV)0.1687591536
Kurtosis34.95202467
Mean176.3001567
Median Absolute Deviation (MAD)5
Skewness3.987870048
Sum224959
Variance885.1972862
MonotonicityNot monotonic
2022-05-21T18:59:28.887697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165432
33.9%
170138
 
10.8%
20075
 
5.9%
18065
 
5.1%
19056
 
4.4%
16354
 
4.2%
20534
 
2.7%
16029
 
2.3%
21028
 
2.2%
20925
 
2.0%
Other values (63)340
26.6%
ValueCountFrequency (%)
1009
0.7%
1081
 
0.1%
1092
 
0.2%
1103
 
0.2%
1121
 
0.1%
1134
0.3%
1191
 
0.1%
1211
 
0.1%
1221
 
0.1%
1241
 
0.1%
ValueCountFrequency (%)
4983
 
0.2%
3075
 
0.4%
295.514
1.1%
2952
 
0.2%
2411
 
0.1%
2381
 
0.1%
2351
 
0.1%
2271
 
0.1%
2255
 
0.4%
2205
 
0.4%

Power_Steering
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Electric Power
924 
Electro-Hydraulic
137 
Yes
 
85
Hydraulic Power
 
72
No
 
57

Length

Max length31
Median length14
Mean length13.12304075
Min length2

Characters and Unicode

Total characters16745
Distinct characters21
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowElectric Power
2nd rowNo
3rd rowElectric Power
4th rowElectric Power
5th rowElectric Power

Common Values

ValueCountFrequency (%)
Electric Power924
72.4%
Electro-Hydraulic137
 
10.7%
Yes85
 
6.7%
Hydraulic Power72
 
5.6%
No57
 
4.5%
Electric Power, Hydraulic Power1
 
0.1%

Length

2022-05-21T18:59:29.189143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-21T18:59:29.512779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
power998
43.9%
electric925
40.7%
electro-hydraulic137
 
6.0%
yes85
 
3.7%
hydraulic73
 
3.2%
no57
 
2.5%

Most occurring characters

ValueCountFrequency (%)
r2270
13.6%
c2197
13.1%
e2145
12.8%
l1272
7.6%
o1192
7.1%
i1135
6.8%
E1062
6.3%
t1062
6.3%
999
6.0%
w998
6.0%
Other values (11)2413
14.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13196
78.8%
Uppercase Letter2412
 
14.4%
Space Separator999
 
6.0%
Dash Punctuation137
 
0.8%
Other Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2270
17.2%
c2197
16.6%
e2145
16.3%
l1272
9.6%
o1192
9.0%
i1135
8.6%
t1062
8.0%
w998
7.6%
y210
 
1.6%
d210
 
1.6%
Other values (3)505
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
E1062
44.0%
P998
41.4%
H210
 
8.7%
Y85
 
3.5%
N57
 
2.4%
Space Separator
ValueCountFrequency (%)
999
100.0%
Dash Punctuation
ValueCountFrequency (%)
-137
100.0%
Other Punctuation
ValueCountFrequency (%)
,1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15608
93.2%
Common1137
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2270
14.5%
c2197
14.1%
e2145
13.7%
l1272
8.1%
o1192
7.6%
i1135
7.3%
E1062
6.8%
t1062
6.8%
w998
6.4%
P998
6.4%
Other values (8)1277
8.2%
Common
ValueCountFrequency (%)
999
87.9%
-137
 
12.0%
,1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII16745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2270
13.6%
c2197
13.1%
e2145
12.8%
l1272
7.6%
o1192
7.1%
i1135
6.8%
E1062
6.3%
t1062
6.3%
999
6.0%
w998
6.0%
Other values (11)2413
14.4%

Power_Windows
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
All Windows
1035 
Only Front Windows
144 
No
 
97

Length

Max length18
Median length11
Mean length11.10579937
Min length2

Characters and Unicode

Total characters14171
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOnly Front Windows
2nd rowNo
3rd rowNo
4th rowOnly Front Windows
5th rowNo

Common Values

ValueCountFrequency (%)
All Windows1035
81.1%
Only Front Windows144
 
11.3%
No97
 
7.6%

Length

2022-05-21T18:59:29.820012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-21T18:59:30.121779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
windows1179
45.4%
all1035
39.8%
only144
 
5.5%
front144
 
5.5%
no97
 
3.7%

Most occurring characters

ValueCountFrequency (%)
l2214
15.6%
n1467
10.4%
o1420
10.0%
1323
9.3%
W1179
8.3%
i1179
8.3%
d1179
8.3%
w1179
8.3%
s1179
8.3%
A1035
7.3%
Other values (6)817
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10249
72.3%
Uppercase Letter2599
 
18.3%
Space Separator1323
 
9.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l2214
21.6%
n1467
14.3%
o1420
13.9%
i1179
11.5%
d1179
11.5%
w1179
11.5%
s1179
11.5%
y144
 
1.4%
r144
 
1.4%
t144
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
W1179
45.4%
A1035
39.8%
O144
 
5.5%
F144
 
5.5%
N97
 
3.7%
Space Separator
ValueCountFrequency (%)
1323
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12848
90.7%
Common1323
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
l2214
17.2%
n1467
11.4%
o1420
11.1%
W1179
9.2%
i1179
9.2%
d1179
9.2%
w1179
9.2%
s1179
9.2%
A1035
8.1%
O144
 
1.1%
Other values (5)673
 
5.2%
Common
ValueCountFrequency (%)
1323
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII14171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l2214
15.6%
n1467
10.4%
o1420
10.0%
1323
9.3%
W1179
8.3%
i1179
8.3%
d1179
8.3%
w1179
8.3%
s1179
8.3%
A1035
7.3%
Other values (6)817
 
5.8%

Power_Seats
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
No
893 
Yes
209 
Yes, with memory
108 
Power seats
 
66

Length

Max length16
Median length2
Mean length3.814263323
Min length2

Characters and Unicode

Total characters4867
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No893
70.0%
Yes209
 
16.4%
Yes, with memory108
 
8.5%
Power seats66
 
5.2%

Length

2022-05-21T18:59:30.834150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-21T18:59:31.095044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
no893
57.3%
yes317
 
20.3%
with108
 
6.9%
memory108
 
6.9%
power66
 
4.2%
seats66
 
4.2%

Most occurring characters

ValueCountFrequency (%)
o1067
21.9%
N893
18.3%
e557
11.4%
s449
9.2%
Y317
 
6.5%
282
 
5.8%
m216
 
4.4%
w174
 
3.6%
t174
 
3.6%
r174
 
3.6%
Other values (6)564
11.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3201
65.8%
Uppercase Letter1276
 
26.2%
Space Separator282
 
5.8%
Other Punctuation108
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o1067
33.3%
e557
17.4%
s449
14.0%
m216
 
6.7%
w174
 
5.4%
t174
 
5.4%
r174
 
5.4%
i108
 
3.4%
h108
 
3.4%
y108
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
N893
70.0%
Y317
 
24.8%
P66
 
5.2%
Space Separator
ValueCountFrequency (%)
282
100.0%
Other Punctuation
ValueCountFrequency (%)
,108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4477
92.0%
Common390
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o1067
23.8%
N893
19.9%
e557
12.4%
s449
10.0%
Y317
 
7.1%
m216
 
4.8%
w174
 
3.9%
t174
 
3.9%
r174
 
3.9%
i108
 
2.4%
Other values (4)348
 
7.8%
Common
ValueCountFrequency (%)
282
72.3%
,108
 
27.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4867
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o1067
21.9%
N893
18.3%
e557
11.4%
s449
9.2%
Y317
 
6.5%
282
 
5.8%
m216
 
4.4%
w174
 
3.6%
t174
 
3.6%
r174
 
3.6%
Other values (6)564
11.6%

Keyless_Entry
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Smart Key
456 
Remote
421 
No
274 
Yes
103 
Remote, Smart Key
 
21

Length

Max length17
Median length9
Mean length6.160658307
Min length2

Characters and Unicode

Total characters7861
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowRemote
2nd rowNo
3rd rowNo
4th rowRemote
5th rowNo

Common Values

ValueCountFrequency (%)
Smart Key456
35.7%
Remote421
33.0%
No274
21.5%
Yes103
 
8.1%
Remote, Smart Key21
 
1.6%
Smart Key, Remote1
 
0.1%

Length

2022-05-21T18:59:31.373008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-21T18:59:31.668735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
smart478
26.9%
key478
26.9%
remote443
24.9%
no274
15.4%
yes103
 
5.8%

Most occurring characters

ValueCountFrequency (%)
e1467
18.7%
m921
11.7%
t921
11.7%
o717
9.1%
500
 
6.4%
S478
 
6.1%
a478
 
6.1%
r478
 
6.1%
K478
 
6.1%
y478
 
6.1%
Other values (5)945
12.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5563
70.8%
Uppercase Letter1776
 
22.6%
Space Separator500
 
6.4%
Other Punctuation22
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1467
26.4%
m921
16.6%
t921
16.6%
o717
12.9%
a478
 
8.6%
r478
 
8.6%
y478
 
8.6%
s103
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
S478
26.9%
K478
26.9%
R443
24.9%
N274
15.4%
Y103
 
5.8%
Space Separator
ValueCountFrequency (%)
500
100.0%
Other Punctuation
ValueCountFrequency (%)
,22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7339
93.4%
Common522
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1467
20.0%
m921
12.5%
t921
12.5%
o717
9.8%
S478
 
6.5%
a478
 
6.5%
r478
 
6.5%
K478
 
6.5%
y478
 
6.5%
R443
 
6.0%
Other values (3)480
 
6.5%
Common
ValueCountFrequency (%)
500
95.8%
,22
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII7861
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1467
18.7%
m921
11.7%
t921
11.7%
o717
9.1%
500
 
6.4%
S478
 
6.1%
a478
 
6.1%
r478
 
6.1%
K478
 
6.1%
y478
 
6.1%
Other values (5)945
12.0%

Power
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct240
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.0108509
Minimum47
Maximum789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-21T18:59:32.018890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile67.048
Q182.824
median108.46
Q3174.522
95-th percentile468.334
Maximum789
Range742
Interquartile range (IQR)91.698

Descriptive statistics

Standard deviation126.4895767
Coefficient of variation (CV)0.8005119647
Kurtosis5.068776594
Mean158.0108509
Median Absolute Deviation (MAD)33.524
Skewness2.290763337
Sum201621.8457
Variance15999.613
MonotonicityNot monotonic
2022-05-21T18:59:32.320726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.04876
 
6.0%
88.7466
 
5.2%
98.657
 
4.5%
108.4655
 
4.3%
81.83852
 
4.1%
73.9538
 
3.0%
138.0424
 
1.9%
84.79623
 
1.8%
152.8322
 
1.7%
103.5322
 
1.7%
Other values (230)841
65.9%
ValueCountFrequency (%)
472
 
0.2%
488
0.6%
53.2448
0.6%
58.1744
0.3%
591
 
0.1%
61.1322
 
0.2%
62.1186
0.5%
63.00541
 
0.1%
63.1045
0.4%
64.095
0.4%
ValueCountFrequency (%)
7891
0.1%
729.641
0.1%
690.22
0.2%
671.4461
0.1%
660.622
0.2%
659.7391
0.1%
659.6341
0.1%
640.91
0.1%
6312
0.2%
630.0542
0.2%

Seating_Capacity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.268808777
Minimum2
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-21T18:59:32.602308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median5
Q35
95-th percentile7
Maximum16
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.142682976
Coefficient of variation (CV)0.2168769117
Kurtosis8.168934939
Mean5.268808777
Median Absolute Deviation (MAD)0
Skewness1.055978334
Sum6723
Variance1.305724384
MonotonicityNot monotonic
2022-05-21T18:59:32.790995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5921
72.2%
7183
 
14.3%
470
 
5.5%
239
 
3.1%
626
 
2.0%
919
 
1.5%
817
 
1.3%
161
 
0.1%
ValueCountFrequency (%)
239
 
3.1%
470
 
5.5%
5921
72.2%
626
 
2.0%
7183
 
14.3%
817
 
1.3%
919
 
1.5%
161
 
0.1%
ValueCountFrequency (%)
161
 
0.1%
919
 
1.5%
817
 
1.3%
7183
 
14.3%
626
 
2.0%
5921
72.2%
470
 
5.5%
239
 
3.1%

Seats_Material
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Fabric
752 
Leather
511 
Vinyl
 
9
Polyurethene
 
4

Length

Max length12
Median length6
Mean length6.412225705
Min length5

Characters and Unicode

Total characters8182
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFabric
2nd rowFabric
3rd rowFabric
4th rowFabric
5th rowFabric

Common Values

ValueCountFrequency (%)
Fabric752
58.9%
Leather511
40.0%
Vinyl9
 
0.7%
Polyurethene4
 
0.3%

Length

2022-05-21T18:59:33.043947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-21T18:59:33.353492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
fabric752
58.9%
leather511
40.0%
vinyl9
 
0.7%
polyurethene4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r1267
15.5%
a1263
15.4%
e1034
12.6%
i761
9.3%
F752
9.2%
b752
9.2%
c752
9.2%
h515
6.3%
t515
6.3%
L511
6.2%
Other values (7)60
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6906
84.4%
Uppercase Letter1276
 
15.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r1267
18.3%
a1263
18.3%
e1034
15.0%
i761
11.0%
b752
10.9%
c752
10.9%
h515
7.5%
t515
7.5%
n13
 
0.2%
y13
 
0.2%
Other values (3)21
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
F752
58.9%
L511
40.0%
V9
 
0.7%
P4
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin8182
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r1267
15.5%
a1263
15.4%
e1034
12.6%
i761
9.3%
F752
9.2%
b752
9.2%
c752
9.2%
h515
6.3%
t515
6.3%
L511
6.2%
Other values (7)60
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII8182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r1267
15.5%
a1263
15.4%
e1034
12.6%
i761
9.3%
F752
9.2%
b752
9.2%
c752
9.2%
h515
6.3%
t515
6.3%
L511
6.2%
Other values (7)60
 
0.7%

Type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Manual
726 
Automatic
522 
AMT
 
18
DCT
 
7
CVT
 
3

Length

Max length9
Median length6
Mean length7.161442006
Min length3

Characters and Unicode

Total characters9138
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowAutomatic
5th rowManual

Common Values

ValueCountFrequency (%)
Manual726
56.9%
Automatic522
40.9%
AMT18
 
1.4%
DCT7
 
0.5%
CVT3
 
0.2%

Length

2022-05-21T18:59:33.617273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-21T18:59:33.905269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
manual726
56.9%
automatic522
40.9%
amt18
 
1.4%
dct7
 
0.5%
cvt3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a1974
21.6%
u1248
13.7%
t1044
11.4%
M744
 
8.1%
n726
 
7.9%
l726
 
7.9%
A540
 
5.9%
o522
 
5.7%
m522
 
5.7%
i522
 
5.7%
Other values (5)570
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7806
85.4%
Uppercase Letter1332
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1974
25.3%
u1248
16.0%
t1044
13.4%
n726
 
9.3%
l726
 
9.3%
o522
 
6.7%
m522
 
6.7%
i522
 
6.7%
c522
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
M744
55.9%
A540
40.5%
T28
 
2.1%
C10
 
0.8%
D7
 
0.5%
V3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin9138
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1974
21.6%
u1248
13.7%
t1044
11.4%
M744
 
8.1%
n726
 
7.9%
l726
 
7.9%
A540
 
5.9%
o522
 
5.7%
m522
 
5.7%
i522
 
5.7%
Other values (5)570
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII9138
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1974
21.6%
u1248
13.7%
t1044
11.4%
M744
 
8.1%
n726
 
7.9%
l726
 
7.9%
A540
 
5.9%
o522
 
5.7%
m522
 
5.7%
i522
 
5.7%
Other values (5)570
 
6.2%

Audiosystem
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
CD Player with USB & Aux-in
484 
CD/MP3/DVD Player with USB & Aux-in
371 
No
213 
DVD Player with USB & Aux-in
101 
USB & Aux-in
84 
Other values (3)
 
23

Length

Max length35
Median length28
Mean length24.01253918
Min length2

Characters and Unicode

Total characters30640
Distinct characters29
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowCD Player with USB & Aux-in
2nd rowNo
3rd rowNo
4th rowCD Player with USB & Aux-in
5th rowNo

Common Values

ValueCountFrequency (%)
CD Player with USB & Aux-in484
37.9%
CD/MP3/DVD Player with USB & Aux-in371
29.1%
No213
16.7%
DVD Player with USB & Aux-in101
 
7.9%
USB & Aux-in84
 
6.6%
CD/MP3 Player20
 
1.6%
CD Player with Aux-in2
 
0.2%
CD Player with USB Only1
 
0.1%

Length

2022-05-21T18:59:34.211600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-21T18:59:34.545136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
aux-in1042
16.7%
usb1041
16.6%
1040
16.6%
player979
15.7%
with959
15.3%
cd487
7.8%
cd/mp3/dvd371
 
5.9%
no213
 
3.4%
dvd101
 
1.6%
cd/mp320
 
0.3%

Most occurring characters

ValueCountFrequency (%)
4978
 
16.2%
i2001
 
6.5%
D1822
 
5.9%
P1370
 
4.5%
n1043
 
3.4%
x1042
 
3.4%
u1042
 
3.4%
A1042
 
3.4%
-1042
 
3.4%
U1041
 
3.4%
Other values (19)14217
46.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13115
42.8%
Uppercase Letter9312
30.4%
Space Separator4978
 
16.2%
Other Punctuation1802
 
5.9%
Dash Punctuation1042
 
3.4%
Decimal Number391
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i2001
15.3%
n1043
8.0%
x1042
7.9%
u1042
7.9%
y980
7.5%
l980
7.5%
r979
7.5%
e979
7.5%
a979
7.5%
w959
7.3%
Other values (3)2131
16.2%
Uppercase Letter
ValueCountFrequency (%)
D1822
19.6%
P1370
14.7%
A1042
11.2%
U1041
11.2%
B1041
11.2%
S1041
11.2%
C878
9.4%
V472
 
5.1%
M391
 
4.2%
N213
 
2.3%
Other Punctuation
ValueCountFrequency (%)
&1040
57.7%
/762
42.3%
Space Separator
ValueCountFrequency (%)
4978
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1042
100.0%
Decimal Number
ValueCountFrequency (%)
3391
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin22427
73.2%
Common8213
 
26.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i2001
 
8.9%
D1822
 
8.1%
P1370
 
6.1%
n1043
 
4.7%
x1042
 
4.6%
u1042
 
4.6%
A1042
 
4.6%
U1041
 
4.6%
B1041
 
4.6%
S1041
 
4.6%
Other values (14)9942
44.3%
Common
ValueCountFrequency (%)
4978
60.6%
-1042
 
12.7%
&1040
 
12.7%
/762
 
9.3%
3391
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII30640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4978
 
16.2%
i2001
 
6.5%
D1822
 
5.9%
P1370
 
4.5%
n1043
 
3.4%
x1042
 
3.4%
u1042
 
3.4%
A1042
 
3.4%
-1042
 
3.4%
U1041
 
3.4%
Other values (19)14217
46.4%

Bluetooth
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
True
984 
False
292 
ValueCountFrequency (%)
True984
77.1%
False292
 
22.9%
2022-05-21T18:59:34.891803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Boot_Space
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct139
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean390.3198637
Minimum20
Maximum1761
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-21T18:59:35.119308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile205.375
Q1295
median390.3198637
Q3460
95-th percentile592
Maximum1761
Range1741
Interquartile range (IQR)165

Descriptive statistics

Standard deviation160.3343007
Coefficient of variation (CV)0.4107766876
Kurtosis19.30038475
Mean390.3198637
Median Absolute Deviation (MAD)74.6801363
Skewness2.826975981
Sum498048.1461
Variance25707.08798
MonotonicityNot monotonic
2022-05-21T18:59:35.410002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
390.3198637249
 
19.5%
35044
 
3.4%
47535
 
2.7%
46031
 
2.4%
51030
 
2.4%
25128
 
2.2%
42024
 
1.9%
24322
 
1.7%
25622
 
1.7%
40021
 
1.6%
Other values (129)770
60.3%
ValueCountFrequency (%)
202
 
0.2%
541
 
0.1%
708
0.6%
942
 
0.2%
9619
1.5%
1107
 
0.5%
1287
 
0.5%
1322
 
0.2%
1331
 
0.1%
1352
 
0.2%
ValueCountFrequency (%)
17612
 
0.2%
17022
 
0.2%
14003
0.2%
10501
 
0.1%
10254
0.3%
9814
0.3%
9096
0.5%
8252
 
0.2%
7701
 
0.1%
7591
 
0.1%

Central_Locking
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
True
1127 
False
149 
ValueCountFrequency (%)
True1127
88.3%
False149
 
11.7%
2022-05-21T18:59:35.711407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Child_Safety_Locks
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
True
1201 
False
 
75
ValueCountFrequency (%)
True1201
94.1%
False75
 
5.9%
2022-05-21T18:59:35.935942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
True
1033 
False
243 
ValueCountFrequency (%)
True1033
81.0%
False243
 
19.0%
2022-05-21T18:59:36.150352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Handbrake
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Manual
970 
Automatic
306 

Length

Max length9
Median length6
Mean length6.719435737
Min length6

Characters and Unicode

Total characters8574
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Manual970
76.0%
Automatic306
 
24.0%

Length

2022-05-21T18:59:36.388499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-21T18:59:36.636094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
manual970
76.0%
automatic306
 
24.0%

Most occurring characters

ValueCountFrequency (%)
a2246
26.2%
u1276
14.9%
M970
11.3%
n970
11.3%
l970
11.3%
t612
 
7.1%
A306
 
3.6%
o306
 
3.6%
m306
 
3.6%
i306
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7298
85.1%
Uppercase Letter1276
 
14.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2246
30.8%
u1276
17.5%
n970
13.3%
l970
13.3%
t612
 
8.4%
o306
 
4.2%
m306
 
4.2%
i306
 
4.2%
c306
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
M970
76.0%
A306
 
24.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8574
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2246
26.2%
u1276
14.9%
M970
11.3%
n970
11.3%
l970
11.3%
t612
 
7.1%
A306
 
3.6%
o306
 
3.6%
m306
 
3.6%
i306
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII8574
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2246
26.2%
u1276
14.9%
M970
11.3%
n970
11.3%
l970
11.3%
t612
 
7.1%
A306
 
3.6%
o306
 
3.6%
m306
 
3.6%
i306
 
3.6%

Instrument_Console
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Analog + Digital
1032 
Analog
168 
Digital
 
76

Length

Max length16
Median length16
Mean length14.14733542
Min length6

Characters and Unicode

Total characters18052
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnalog + Digital
2nd rowAnalog + Digital
3rd rowAnalog + Digital
4th rowAnalog + Digital
5th rowAnalog + Digital

Common Values

ValueCountFrequency (%)
Analog + Digital1032
80.9%
Analog168
 
13.2%
Digital76
 
6.0%

Length

2022-05-21T18:59:36.866773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-21T18:59:37.129791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
analog1200
35.9%
digital1108
33.2%
1032
30.9%

Most occurring characters

ValueCountFrequency (%)
a2308
12.8%
l2308
12.8%
g2308
12.8%
i2216
12.3%
2064
11.4%
A1200
6.6%
n1200
6.6%
o1200
6.6%
D1108
6.1%
t1108
6.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12648
70.1%
Uppercase Letter2308
 
12.8%
Space Separator2064
 
11.4%
Math Symbol1032
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2308
18.2%
l2308
18.2%
g2308
18.2%
i2216
17.5%
n1200
9.5%
o1200
9.5%
t1108
8.8%
Uppercase Letter
ValueCountFrequency (%)
A1200
52.0%
D1108
48.0%
Space Separator
ValueCountFrequency (%)
2064
100.0%
Math Symbol
ValueCountFrequency (%)
+1032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14956
82.8%
Common3096
 
17.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2308
15.4%
l2308
15.4%
g2308
15.4%
i2216
14.8%
A1200
8.0%
n1200
8.0%
o1200
8.0%
D1108
7.4%
t1108
7.4%
Common
ValueCountFrequency (%)
2064
66.7%
+1032
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII18052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2308
12.8%
l2308
12.8%
g2308
12.8%
i2216
12.3%
2064
11.4%
A1200
6.6%
n1200
6.6%
o1200
6.6%
D1108
6.1%
t1108
6.1%

Ventilation_System
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Fully automatic climate control
487 
Manual Air conditioning with cooling and heating
447 
2 Zone Climate Control
148 
4 Zone climate control
59 
3 Zone climate control
 
35
Other values (11)
100 

Length

Max length81
Median length56
Mean length34.82915361
Min length2

Characters and Unicode

Total characters44442
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowManual Air conditioning with cooling and heating
2nd rowNo
3rd rowAir Conditioning with cooling only
4th rowManual Air conditioning with cooling and heating
5th rowAir Conditioning with cooling only

Common Values

ValueCountFrequency (%)
Fully automatic climate control487
38.2%
Manual Air conditioning with cooling and heating447
35.0%
2 Zone Climate Control148
 
11.6%
4 Zone climate control59
 
4.6%
3 Zone climate control35
 
2.7%
No35
 
2.7%
Air Conditioning with cooling only22
 
1.7%
Fully automatic climate control, 2 Zone Climate Control10
 
0.8%
Yes8
 
0.6%
Heater, Manual Air conditioning with cooling and heating6
 
0.5%
Other values (6)19
 
1.5%

Length

2022-05-21T18:59:37.398930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
climate773
12.0%
control773
12.0%
fully512
7.9%
automatic512
7.9%
air477
7.4%
conditioning477
7.4%
with477
7.4%
cooling477
7.4%
heating455
7.1%
and455
7.1%
Other values (9)1056
16.4%

Most occurring characters

ValueCountFrequency (%)
5168
11.6%
i4602
10.4%
n4329
9.7%
o4284
9.6%
t3993
9.0%
a3631
 
8.2%
l3524
 
7.9%
c2672
 
6.0%
e1525
 
3.4%
u1479
 
3.3%
Other values (20)9235
20.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter36880
83.0%
Space Separator5168
 
11.6%
Uppercase Letter2102
 
4.7%
Decimal Number261
 
0.6%
Other Punctuation31
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i4602
12.5%
n4329
11.7%
o4284
11.6%
t3993
10.8%
a3631
9.8%
l3524
9.6%
c2672
7.2%
e1525
 
4.1%
u1479
 
4.0%
g1409
 
3.8%
Other values (7)5432
14.7%
Uppercase Letter
ValueCountFrequency (%)
F512
24.4%
A477
22.7%
M455
21.6%
C340
16.2%
Z261
12.4%
N35
 
1.7%
H14
 
0.7%
Y8
 
0.4%
Decimal Number
ValueCountFrequency (%)
2159
60.9%
465
24.9%
337
 
14.2%
Space Separator
ValueCountFrequency (%)
5168
100.0%
Other Punctuation
ValueCountFrequency (%)
,31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin38982
87.7%
Common5460
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i4602
11.8%
n4329
11.1%
o4284
11.0%
t3993
10.2%
a3631
9.3%
l3524
9.0%
c2672
 
6.9%
e1525
 
3.9%
u1479
 
3.8%
g1409
 
3.6%
Other values (15)7534
19.3%
Common
ValueCountFrequency (%)
5168
94.7%
2159
 
2.9%
465
 
1.2%
337
 
0.7%
,31
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII44442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5168
11.6%
i4602
10.4%
n4329
9.7%
o4284
9.6%
t3993
9.0%
a3631
 
8.2%
l3524
 
7.9%
c2672
 
6.0%
e1525
 
3.4%
u1479
 
3.3%
Other values (20)9235
20.8%

Engine_Immobilizer
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
True
1216 
False
 
60
ValueCountFrequency (%)
True1216
95.3%
False60
 
4.7%
2022-05-21T18:59:37.705086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

ABS_(Anti-lock_Braking_System)
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
True
1144 
False
132 
ValueCountFrequency (%)
True1144
89.7%
False132
 
10.3%
2022-05-21T18:59:37.965262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Door_Ajar_Warning
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
True
1133 
False
143 
ValueCountFrequency (%)
True1133
88.8%
False143
 
11.2%
2022-05-21T18:59:38.228392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

EBD_(Electronic_Brake-force_Distribution)
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
True
1075 
False
201 
ValueCountFrequency (%)
True1075
84.2%
False201
 
15.8%
2022-05-21T18:59:38.497445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Fasten_Seat_Belt_Warning
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
True
1086 
False
190 
ValueCountFrequency (%)
True1086
85.1%
False190
 
14.9%
2022-05-21T18:59:38.766984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Parking_Assistance
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Rear sensors with camera
367 
No
290 
Rear sensors
277 
Front and rear sensors with camera
181 
Front & rear sensors with 360 degree view
120 
Other values (6)
41 

Length

Max length77
Median length41
Mean length19.46003135
Min length2

Characters and Unicode

Total characters24831
Distinct characters25
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
Rear sensors with camera367
28.8%
No290
22.7%
Rear sensors277
21.7%
Front and rear sensors with camera181
14.2%
Front & rear sensors with 360 degree view120
 
9.4%
Rear sensors, Rear sensors with camera17
 
1.3%
Yes15
 
1.2%
Front sensors, Rear sensors5
 
0.4%
Front and rear sensors with camera, Front & rear sensors with 360 degree view2
 
0.2%
Rear sensors with camera, Rear sensors1
 
0.1%

Length

2022-05-21T18:59:39.054102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sensors997
22.0%
rear991
21.9%
with690
15.2%
camera568
12.5%
front311
 
6.9%
no290
 
6.4%
and183
 
4.0%
122
 
2.7%
360122
 
2.7%
degree122
 
2.7%
Other values (2)137
 
3.0%

Most occurring characters

ValueCountFrequency (%)
r3294
13.3%
3257
13.1%
e3059
12.3%
s3006
12.1%
a2310
9.3%
o1598
 
6.4%
n1491
 
6.0%
t1001
 
4.0%
w812
 
3.3%
i812
 
3.3%
Other values (15)4191
16.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter19758
79.6%
Space Separator3257
 
13.1%
Uppercase Letter1302
 
5.2%
Decimal Number366
 
1.5%
Other Punctuation148
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r3294
16.7%
e3059
15.5%
s3006
15.2%
a2310
11.7%
o1598
8.1%
n1491
7.5%
t1001
 
5.1%
w812
 
4.1%
i812
 
4.1%
h690
 
3.5%
Other values (5)1685
8.5%
Uppercase Letter
ValueCountFrequency (%)
R686
52.7%
F311
23.9%
N290
22.3%
Y15
 
1.2%
Decimal Number
ValueCountFrequency (%)
3122
33.3%
6122
33.3%
0122
33.3%
Other Punctuation
ValueCountFrequency (%)
&122
82.4%
,26
 
17.6%
Space Separator
ValueCountFrequency (%)
3257
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin21060
84.8%
Common3771
 
15.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r3294
15.6%
e3059
14.5%
s3006
14.3%
a2310
11.0%
o1598
7.6%
n1491
7.1%
t1001
 
4.8%
w812
 
3.9%
i812
 
3.9%
h690
 
3.3%
Other values (9)2987
14.2%
Common
ValueCountFrequency (%)
3257
86.4%
&122
 
3.2%
3122
 
3.2%
6122
 
3.2%
0122
 
3.2%
,26
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII24831
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r3294
13.3%
3257
13.1%
e3059
12.3%
s3006
12.1%
a2310
9.3%
o1598
 
6.4%
n1491
 
6.0%
t1001
 
4.0%
w812
 
3.3%
i812
 
3.3%
Other values (15)4191
16.9%

EBA_(Electronic_Brake_Assist)
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
690 
True
586 
ValueCountFrequency (%)
False690
54.1%
True586
45.9%
2022-05-21T18:59:39.387971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Seat_Height_Adjustment
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Manual Adjustment
855 
Electric Adjustment with Memory
239 
Electric Adjustment
168 
Semi Automatic Adjustment
 
14

Length

Max length31
Median length17
Mean length19.97335423
Min length17

Characters and Unicode

Total characters25486
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual Adjustment
2nd rowManual Adjustment
3rd rowManual Adjustment
4th rowManual Adjustment
5th rowManual Adjustment

Common Values

ValueCountFrequency (%)
Manual Adjustment855
67.0%
Electric Adjustment with Memory239
 
18.7%
Electric Adjustment168
 
13.2%
Semi Automatic Adjustment14
 
1.1%

Length

2022-05-21T18:59:39.624504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-21T18:59:39.971697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
adjustment1276
41.9%
manual855
28.1%
electric407
 
13.4%
with239
 
7.9%
memory239
 
7.9%
semi14
 
0.5%
automatic14
 
0.5%

Most occurring characters

ValueCountFrequency (%)
t3226
12.7%
u2145
 
8.4%
n2131
 
8.4%
e1936
 
7.6%
1768
 
6.9%
a1724
 
6.8%
m1543
 
6.1%
A1290
 
5.1%
d1276
 
5.0%
j1276
 
5.0%
Other values (12)7171
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter20913
82.1%
Uppercase Letter2805
 
11.0%
Space Separator1768
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t3226
15.4%
u2145
10.3%
n2131
10.2%
e1936
9.3%
a1724
8.2%
m1543
7.4%
d1276
 
6.1%
j1276
 
6.1%
s1276
 
6.1%
l1262
 
6.0%
Other values (7)3118
14.9%
Uppercase Letter
ValueCountFrequency (%)
A1290
46.0%
M1094
39.0%
E407
 
14.5%
S14
 
0.5%
Space Separator
ValueCountFrequency (%)
1768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin23718
93.1%
Common1768
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t3226
13.6%
u2145
 
9.0%
n2131
 
9.0%
e1936
 
8.2%
a1724
 
7.3%
m1543
 
6.5%
A1290
 
5.4%
d1276
 
5.4%
j1276
 
5.4%
s1276
 
5.4%
Other values (11)5895
24.9%
Common
ValueCountFrequency (%)
1768
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII25486
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t3226
12.7%
u2145
 
8.4%
n2131
 
8.4%
e1936
 
7.6%
1768
 
6.9%
a1724
 
6.8%
m1543
 
6.1%
A1290
 
5.1%
d1276
 
5.0%
j1276
 
5.0%
Other values (12)7171
28.1%

Interactions

2022-05-21T18:59:09.401549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:21.667701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:25.525426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:29.489614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:33.774659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:37.624712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:41.327909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:45.098840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:48.991446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:52.904530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:57.475838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:01.423973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:05.303836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:09.696326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:21.978309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:25.827148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:29.830980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:34.073551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:37.931602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:41.593678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:45.371368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:49.264439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:53.199552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:57.754721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:01.696251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:05.594963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:10.007037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:22.270974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:26.123866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:30.099451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:34.354113image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:38.193296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:41.997042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:45.672305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:49.546546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:53.485660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:58.038044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:01.985645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:05.916553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:10.337563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:22.570748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:26.451309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:30.830175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:34.654930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:38.465452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:42.285117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:46.003604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:49.882333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:53.848315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:58.353575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:02.340270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:06.265212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:10.640654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:22.856982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:26.841550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:31.124034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:34.920086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:38.737186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:42.573745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:46.273720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:50.166802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:54.181057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:58.702541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:02.639783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:06.595776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:10.960878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:23.113614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:27.139379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:31.386539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:35.175435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:39.021816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:42.825194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:46.554843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:50.424606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:54.516843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:58.967213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:02.936202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:06.888919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:11.262572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:23.380822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:27.393725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:31.649763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:35.464527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:39.313058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:43.084078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:46.839795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:50.752052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:54.888424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:59.253885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:03.209634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:07.153084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:11.594526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:23.657939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:27.689562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:31.923782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:35.787157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:39.591587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:43.365582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:47.162336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:51.029035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:55.279074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:59.594227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:03.493774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:07.421109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:11.921365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:23.911664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:27.949358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:32.180168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:36.054221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:39.848892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:43.610965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:47.431222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:51.268688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:55.589797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:59.910704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:03.758843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:07.708038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:12.257380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:24.197586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:28.260569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:32.471124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:36.355029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:40.152559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:43.907337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:47.723494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:51.616769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:55.955090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:00.239357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:04.097894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:08.081761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:12.552198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:24.508873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:28.589479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:32.791972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:36.690603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:40.434472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:44.238111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:48.055751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:52.010988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:56.565564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:00.548369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:04.444898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:08.497458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:12.897447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:24.838277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:28.904636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:33.106535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:37.015767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:40.738779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:44.517302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:48.348897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:52.284794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:56.888443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:00.835690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:04.736451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:08.823199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:13.230359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:25.204979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:29.217248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:33.426220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:37.320460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:41.036567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:44.836629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:48.674350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:52.573112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:58:57.197321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:01.124823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:05.017351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:59:09.108883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-05-21T18:59:40.312479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-21T18:59:41.018512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-21T18:59:41.511516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-21T18:59:42.087842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-21T18:59:43.071112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-21T18:59:14.268819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-21T18:59:18.622819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0MakeModelVariantEx-Showroom_PriceDisplacementCylindersEmission_NormFuel_SystemFuel_Tank_CapacityFuel_TypeHeightLengthWidthBody_TypeARAI_Certified_MileageGearsGround_ClearancePower_SteeringPower_WindowsPower_SeatsKeyless_EntryPowerSeating_CapacitySeats_MaterialTypeAudiosystemBluetoothBoot_SpaceCentral_LockingChild_Safety_LocksDistance_to_EmptyHandbrakeInstrument_ConsoleVentilation_SystemEngine_ImmobilizerABS_(Anti-lock_Braking_System)Door_Ajar_WarningEBD_(Electronic_Brake-force_Distribution)Fasten_Seat_Belt_WarningParking_AssistanceEBA_(Electronic_Brake_Assist)Seat_Height_Adjustment
00TataNano GenxXt292667624.02BS IVInjection24.0Petrol1652.03164.01750.0Hatchback23.6000004180.0Electric PowerOnly Front WindowsNoRemote374.6804FabricManualCD Player with USB & Aux-inYes110.0YesYesYesManualAnalog + DigitalManual Air conditioning with cooling and heatingNoNoNoNoNoNoNoManual Adjustment
11TataNano GenxXe236447624.02BS IVInjection24.0Petrol1652.03164.01750.0Hatchback23.6000004180.0NoNoNoNo374.6804FabricManualNoNo110.0NoYesYesManualAnalog + DigitalNoNoNoNoNoNoNoNoManual Adjustment
22TataNano GenxEmax Xm296661624.02BS IVInjection15.0CNG1652.03164.01750.0Hatchback19.9021174180.0Electric PowerNoNoNo374.6804FabricManualNoNo110.0NoYesYesManualAnalog + DigitalAir Conditioning with cooling onlyNoNoNoNoNoNoNoManual Adjustment
33TataNano GenxXta334768624.02BS IVInjection24.0Petrol1652.03164.01750.0Hatchback21.9000005180.0Electric PowerOnly Front WindowsNoRemote374.6804FabricAutomaticCD Player with USB & Aux-inYes94.0YesYesYesManualAnalog + DigitalManual Air conditioning with cooling and heatingNoNoNoNoNoNoNoManual Adjustment
44TataNano GenxXm272223624.02BS IVInjection24.0Petrol1652.03164.01750.0Hatchback23.6000004180.0Electric PowerNoNoNo374.6804FabricManualNoNo110.0NoYesYesManualAnalog + DigitalAir Conditioning with cooling onlyNoNoNoNoNoNoNoManual Adjustment
55TataNano GenxXma314815624.02BS IVInjection24.0Petrol1652.03164.01750.0Hatchback21.9000005180.0Electric PowerNoNoNo374.6804FabricAutomaticNoNo94.0NoYesYesManualAnalog + DigitalAir Conditioning with cooling onlyNoNoYesNoNoNoNoManual Adjustment
66DatsunRedi-GoD279650799.03BS IVInjection28.0Petrol1541.03429.01560.0Hatchback25.1700005185.0NoNoNoNo53.2445FabricManualNoNo222.0NoYesYesManualAnalog + DigitalYesYesYesNoNoNoNoNoManual Adjustment
77DatsunRedi-GoT351832799.03BS IVInjection28.0Petrol1541.03429.01560.0Hatchback25.1700005185.0Electric PowerOnly Front WindowsNoNo53.2445FabricManualCD Player with USB & Aux-inNo222.0YesYesYesManualAnalog + DigitalManual Air conditioning with cooling and heatingYesNoNoNoNoNoNoManual Adjustment
88DatsunRedi-GoA333419799.03BS IVInjection28.0Petrol1541.03429.01560.0Hatchback25.1700005185.0Electric PowerNoNoNo53.2445FabricManualNoNo222.0NoYesYesManualAnalog + DigitalManual Air conditioning with cooling and heatingYesNoNoNoNoNoNoManual Adjustment
99DatsunRedi-GoS362000799.03BS IVInjection28.0Petrol1541.03429.01560.0Hatchback25.1700005185.0Electric PowerNoNoNo53.2445FabricManualCD Player with USB & Aux-inNo222.0NoYesYesManualAnalog + DigitalManual Air conditioning with cooling and heatingYesNoNoNoNoRear sensorsNoManual Adjustment

Last rows

Unnamed: 0MakeModelVariantEx-Showroom_PriceDisplacementCylindersEmission_NormFuel_SystemFuel_Tank_CapacityFuel_TypeHeightLengthWidthBody_TypeARAI_Certified_MileageGearsGround_ClearancePower_SteeringPower_WindowsPower_SeatsKeyless_EntryPowerSeating_CapacitySeats_MaterialTypeAudiosystemBluetoothBoot_SpaceCentral_LockingChild_Safety_LocksDistance_to_EmptyHandbrakeInstrument_ConsoleVentilation_SystemEngine_ImmobilizerABS_(Anti-lock_Braking_System)Door_Ajar_WarningEBD_(Electronic_Brake-force_Distribution)Fasten_Seat_Belt_WarningParking_AssistanceEBA_(Electronic_Brake_Assist)Seat_Height_Adjustment
12661266HondaCityV Mt Petrol10659001497.04BS VIInjection40.0Petrol1495.04440.01695.0Sedan17.805165.0Electric PowerAll WindowsNoRemote117.3345FabricManualCD Player with USB & Aux-inYes510.0YesYesYesManualAnalog + DigitalFully automatic climate controlYesYesYesYesYesFront and rear sensors with cameraNoManual Adjustment
12671267HondaCityVx Mt Petrol11820001497.04BS VIInjection40.0Petrol1495.04440.01695.0Sedan17.405165.0Electric PowerAll WindowsNoSmart Key117.3345LeatherManualDVD Player with USB & Aux-inYes510.0YesYesYesManualAnalog + DigitalFully automatic climate controlYesYesYesYesYesFront and rear sensors with cameraNoManual Adjustment
12681268HondaCityVx Cvt Petrol13120001497.04BS VIInjection40.0Petrol1495.04440.01695.0Sedan18.005165.0Electric PowerAll WindowsNoSmart Key117.3345LeatherAutomaticCD/MP3/DVD Player with USB & Aux-inYes510.0YesYesYesManualAnalog + DigitalFully automatic climate controlYesYesYesYesYesFront and rear sensors with cameraNoManual Adjustment
12691269HondaCitySv Mt Diesel11110001498.04BS IVInjection40.0Diesel1495.04440.01695.0Sedan25.606165.0Electric PowerAll WindowsNoRemote98.6005FabricManualUSB & Aux-inYes510.0YesYesYesManualAnalog + DigitalFully automatic climate controlYesYesYesYesYesNoNoManual Adjustment
12701270HondaCityV Mt Diesel11910001498.04BS IVInjection40.0Diesel1495.04440.01695.0Sedan25.606165.0Electric PowerAll WindowsNoRemote98.6005FabricManualCD Player with USB & Aux-inYes510.0YesYesYesManualAnalog + DigitalFully automatic climate controlYesYesYesYesYesFront and rear sensors with cameraNoManual Adjustment
12711271HondaCityVx Mt Diesel13020001498.04BS IVInjection40.0Diesel1495.04440.01695.0Sedan25.106165.0Electric PowerAll WindowsNoSmart Key98.6005LeatherManualDVD Player with USB & Aux-inYes510.0YesYesYesManualAnalog + DigitalFully automatic climate controlYesYesYesYesYesFront and rear sensors with cameraNoManual Adjustment
12721272HondaCityZx Mt Diesel14210001498.04BS IVInjection40.0Diesel1495.04440.01695.0Sedan25.106165.0Electric PowerAll WindowsNoSmart Key98.6005LeatherManualDVD Player with USB & Aux-inYes510.0YesYesYesManualAnalog + DigitalFully automatic climate controlYesYesYesYesYesRear sensors with cameraNoManual Adjustment
12731273HondaCityZx Cvt Petrol14310001497.04BS VIInjection40.0Petrol1495.04440.01695.0Sedan22.605165.0Electric PowerAll WindowsNoSmart Key117.3345LeatherAutomaticDVD Player with USB & Aux-inYes510.0YesYesYesManualAnalog + DigitalFully automatic climate controlYesYesYesYesYesRear sensors with cameraNoManual Adjustment
12741274HondaCityV Cvt Petrol12010001497.04BS VIInjection40.0Petrol1495.04440.01695.0Sedan17.805165.0Electric PowerAll WindowsNoRemote117.3345FabricAutomaticCD Player with USB & Aux-inYes510.0YesYesYesManualAnalog + DigitalFully automatic climate controlYesYesYesYesYesFront and rear sensors with cameraNoManual Adjustment
12751275MitsubishiMontero3.2 At68625603200.04BS IVInjection88.0Diesel1900.04900.01875.0SUV11.565235.0Electric PowerAll WindowsYesYes199.1727LeatherAutomaticCD Player with USB & Aux-inYes1050.0YesYesYesManualAnalogFully automatic climate controlYesYesYesYesYesRear sensors with cameraYesElectric Adjustment